Background The contamination of public baths with Legionella pneumophila contamination has become a growing public health concern in recent years. However, research on its association with bacterial community characteristics in water samples remains limited. The integration of 16S rRNA sequencing and random forest modeling provides a new approach to elucidate the bacterial community characteristics of public bath water and their association with Legionella pneumophila contamination. Objective To investigate the bacterial community structure and diversity of public bath water in Shanghai, explore the association between Legionella pneumophila contamination and bacterial community characteristics, and identify key bacterial genera associated with contamination, thereby providing a scientific basis for formulating hygiene management regulations for public bath water. Methods From February to March 2023, water samples were collected from ten public baths in Shanghai which were selected based on business scale, regional distribution, and functional differences. Water quality parameters were evaluated, and the samples were categorized into Legionella-positive and Legionella-negative groups based on the detection results of Legionella pneumophila. The bacterial community structure, α-diversity, and β-diversity were analyzed using 16S rRNA sequencing. Redundancy analysis (RDA) was employed to examine the relationship between physicochemical factors and bacterial community diversity. A random forest model was employed to identify key bacterial genera distinguishing the two groups, with the importance of genera being evaluated based on the mean decrease accuracy (MDA). Results The oxygen consumption in the Legionella-positive group was significantly lower than that in the Legionella-negative group (mean values: 1.85 mg·L⁻¹ vs. 6.81 mg·L⁻¹, P< 0.05), while no significant differences were observed in other physicochemical indicators. The sequencing results revealed a total of 27 bacterial phyla and 454 bacterial genera, with Proteobacteria (63.00%) being the dominant phylum. The dominant genera included Pelomonas (8.50%), Acidovorax (8.13%), Mycobacterium (7.93%), and Acinetobacter (6.59%). The α-diversity analysis indicated that bacterial community richness (Chao1 and ACE indices) was significantly higher in the Legionella-positive group than in the Legionella-negative group (P<0.01). The β-diversity analysis showed no significant difference in the bacterial community structure between the two groups (P>0.05). The RDA analysis demonstrated that the bacterial community diversity was positively correlated with pH and negatively correlated with oxygen consumption and free residual chlorine. The RDA1 and RDA2 explained 23.92% and 21.30% of the bacterial community diversity, respectively. The random forest model identified 20 key genera significantly influencing the microbial community distribution between the two groups, including unclassified_Bradyrhizobiaceae (MDA=2.42), Meiothermus (MDA=2.37), and Flavihumibacter (MDA=2.26). Conclusion The diversity of bacterial communities in public bath water is influenced by pH, oxygen consumption, and free residual chlorine. Samples contaminated with Legionella pneumophila exhibit greater microbial richness and contain characteristic key bacterial genera that contribute to community differences. Machine learning random forest technology helps identify these distinctive key bacterial genera. The findings provide a basis for carrying out risk early warning strategies in such settings.

Objective This study aims to assess the impact of four-dimensional cone-beam computed tomography (4D-CBCT) image guidance on setup error correction and target dose distribution optimization in patients receiving stereotactic body radiotherapy for lung cancer. Methods A total of 82 lung cancer patients treated with SBRT in our hospital between February 2023 and February 2025 were randomly selected as study subjects. Using a random number table, they were divided into two groups. The conventional group underwent positioning verification using traditional three-dimensional cone-beam CT, while the study group underwent verification using 4D-CBCT. Setup errors, target dose-volume parameters, organ at risk dose-volume parameters, and adverse reactions were compared between the two groups. Results The setup errors in the X, Y, and Z axes in the study group were 0.21, 0.19, and 0.23 mm, respectively, which were significantly lower than 0.58, 0.62, and 0.59 mm in the conventional group (P<0.05). The three-dimensional vector error was 0.34 (0.21, 0.48) mm in the study group, which was significantly lower than 0.98 (0.65, 1.32) mm in the conventional group (P<0.05). The rotational errors in left-right, anteroposterior, and superior-inferior directions in the study group were (0.25±0.12)°, (0.21±0.10)°, and (0.23±0.11)°, respectively; these values were significantly lower than (0.48±0.18)°, (0.42±0.15)°, and (0.45±0.16)° in the conventional group (P<0.05). The minimum dose received by 95% of the target volume [(59.45±0.85) Gy] and the percentage of the target volume receiving at least 100% of the prescription dose [(95.82±2.65)%] in the study group were higher than those in the conventional group [(58.90±1.20) Gy and (93.65±3.85)%]. The homogeneity index and conformity index in the study group were 1.07±0.03 and 1.15±0.05, respectively, which were lower than 1.12±0.05 and 1.23±0.08 in the conventional group (P<0.05). No significant differences were observed between the two groups in the volume of the lung receiving 5 Gy [(28.11±5.52)% vs. (28.85±6.24)%], the volume of the lung receiving 20 Gy [(12.84±3.56)% vs. (13.45±3.90)%], mean lung dose [(8.65±2.11) Gy vs. (8.95±2.34) Gy], maximum spinal cord dose [(18.54±3.62) Gy vs. (19.13±4.25) Gy], or mean heart dose[(14.85±4.22) Gy vs. (16.27±4.83) Gy](P>0.05). The incidence rates of radiation pneumonitis, radiation esophagitis, and skin reactions in the study group were 12.20%, 7.32%, and 4.88%, respectively, which were not significantly different from 17.07%, 9.76%, and 7.32% in the conventional group (P>0.05). Conclusion The 4D-CBCT can reduce setup errors, improve target dose accuracy and conformity in lung cancer patients undergoing SBRT, and minimize radiation exposure to normal tissues, making it worthy of clinical promotion.

Objective This study aims to assess the impact of four-dimensional cone-beam computed tomography (4D-CBCT) image guidance on setup error correction and target dose distribution optimization in patients receiving stereotactic body radiotherapy for lung cancer. Methods A total of 82 lung cancer patients treated with SBRT in our hospital between February 2023 and February 2025 were randomly selected as study subjects. Using a random number table, they were divided into two groups. The conventional group underwent positioning verification using traditional three-dimensional cone-beam CT, while the study group underwent verification using 4D-CBCT. Setup errors, target dose-volume parameters, organ at risk dose-volume parameters, and adverse reactions were compared between the two groups. Results The setup errors in the X, Y, and Z axes in the study group were 0.21, 0.19, and 0.23 mm, respectively, which were significantly lower than 0.58, 0.62, and 0.59 mm in the conventional group (P<0.05). The three-dimensional vector error was 0.34 (0.21, 0.48) mm in the study group, which was significantly lower than 0.98 (0.65, 1.32) mm in the conventional group (P<0.05). The rotational errors in left-right, anteroposterior, and superior-inferior directions in the study group were (0.25±0.12)°, (0.21±0.10)°, and (0.23±0.11)°, respectively; these values were significantly lower than (0.48±0.18)°, (0.42±0.15)°, and (0.45±0.16)° in the conventional group (P<0.05). The minimum dose received by 95% of the target volume [(59.45±0.85) Gy] and the percentage of the target volume receiving at least 100% of the prescription dose [(95.82±2.65)%] in the study group were higher than those in the conventional group [(58.90±1.20) Gy and (93.65±3.85)%]. The homogeneity index and conformity index in the study group were 1.07±0.03 and 1.15±0.05, respectively, which were lower than 1.12±0.05 and 1.23±0.08 in the conventional group (P<0.05). No significant differences were observed between the two groups in the volume of the lung receiving 5 Gy [(28.11±5.52)% vs. (28.85±6.24)%], the volume of the lung receiving 20 Gy [(12.84±3.56)% vs. (13.45±3.90)%], mean lung dose [(8.65±2.11) Gy vs. (8.95±2.34) Gy], maximum spinal cord dose [(18.54±3.62) Gy vs. (19.13±4.25) Gy], or mean heart dose[(14.85±4.22) Gy vs. (16.27±4.83) Gy](P>0.05). The incidence rates of radiation pneumonitis, radiation esophagitis, and skin reactions in the study group were 12.20%, 7.32%, and 4.88%, respectively, which were not significantly different from 17.07%, 9.76%, and 7.32% in the conventional group (P>0.05). Conclusion The 4D-CBCT can reduce setup errors, improve target dose accuracy and conformity in lung cancer patients undergoing SBRT, and minimize radiation exposure to normal tissues, making it worthy of clinical promotion.

Objective This study aims to assess the impact of four-dimensional cone-beam computed tomography (4D-CBCT) image guidance on setup error correction and target dose distribution optimization in patients receiving stereotactic body radiotherapy for lung cancer. Methods A total of 82 lung cancer patients treated with SBRT in our hospital between February 2023 and February 2025 were randomly selected as study subjects. Using a random number table, they were divided into two groups. The conventional group underwent positioning verification using traditional three-dimensional cone-beam CT, while the study group underwent verification using 4D-CBCT. Setup errors, target dose-volume parameters, organ at risk dose-volume parameters, and adverse reactions were compared between the two groups. Results The setup errors in the X, Y, and Z axes in the study group were 0.21, 0.19, and 0.23 mm, respectively, which were significantly lower than 0.58, 0.62, and 0.59 mm in the conventional group (P<0.05). The three-dimensional vector error was 0.34 (0.21, 0.48) mm in the study group, which was significantly lower than 0.98 (0.65, 1.32) mm in the conventional group (P<0.05). The rotational errors in left-right, anteroposterior, and superior-inferior directions in the study group were (0.25±0.12)°, (0.21±0.10)°, and (0.23±0.11)°, respectively; these values were significantly lower than (0.48±0.18)°, (0.42±0.15)°, and (0.45±0.16)° in the conventional group (P<0.05). The minimum dose received by 95% of the target volume [(59.45±0.85) Gy] and the percentage of the target volume receiving at least 100% of the prescription dose [(95.82±2.65)%] in the study group were higher than those in the conventional group [(58.90±1.20) Gy and (93.65±3.85)%]. The homogeneity index and conformity index in the study group were 1.07±0.03 and 1.15±0.05, respectively, which were lower than 1.12±0.05 and 1.23±0.08 in the conventional group (P<0.05). No significant differences were observed between the two groups in the volume of the lung receiving 5 Gy [(28.11±5.52)% vs. (28.85±6.24)%], the volume of the lung receiving 20 Gy [(12.84±3.56)% vs. (13.45±3.90)%], mean lung dose [(8.65±2.11) Gy vs. (8.95±2.34) Gy], maximum spinal cord dose [(18.54±3.62) Gy vs. (19.13±4.25) Gy], or mean heart dose[(14.85±4.22) Gy vs. (16.27±4.83) Gy](P>0.05). The incidence rates of radiation pneumonitis, radiation esophagitis, and skin reactions in the study group were 12.20%, 7.32%, and 4.88%, respectively, which were not significantly different from 17.07%, 9.76%, and 7.32% in the conventional group (P>0.05). Conclusion The 4D-CBCT can reduce setup errors, improve target dose accuracy and conformity in lung cancer patients undergoing SBRT, and minimize radiation exposure to normal tissues, making it worthy of clinical promotion.

ObjectiveTo study the effect of Taikong Yangxin Pills on the metabolism of simulated weightless rats based on metabolomics and discuss the metabolism mechanism. MethodsIn the simulated space capsule environment on the ground， the rat model of simulated weightlessness was established by the tail suspension method. Rats were randomly grouped as follows： out-of-capsule control， in-capsule control， model， and high （3.0 g·kg^-1） and low （1.5 g·kg^-1） doses of Taikong Yangxin Pills， and they were administrated with corresponding drugs by gavage for 28 days. The serum levels of endogenous metabolites in rats were determined by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry （UPLC-Q-TOF/MS）. The obtained data were processed by principal component analysis （PCA） and orthogonal partial least squares-discriminant analysis （OPLS-DA） to screen for differential metabolites and potential biomarkers. MetaboAnalyst 5.0 was used for pathway enrichment analysis to explain the metabolic regulation mechanism of the drug. ResultsCompared with the out-of-capsule control group， the in-capsule control group showed elevated levels of thirteen metabolites， including 14-hydroxyhexadecanoic acid， linoleic acid， and α-linolenic acid （P<0.05）， which suggested that the space capsule environment mainly affected the metabolism of α-linolenic acid and linoleic acid in the rats. Compared with the in-capsule control group， the model group showed lowered levels of fourteen metabolites， including 4-imidazolone-5-propionic acid， isocitric acid/citric acid， and L-tyrosine （P<0.05）， which were recovered after the treatment with Taikong Yangxin pills （P<0.05）. The pathway enrichment analysis revealed that weightlessness induced by tail suspension and drug intervention mainly involved the phenylalanine， tyrosine， and tryptophan biosynthesis， tyrosine metabolism， histidine metabolism， and citric acid cycle. ConclusionThe simulated space capsule environment and simulated weightlessness induced by tail suspension can both affect the metabolism level of rats. Taikong Yangxin pills can ameliorate the metabolic abnormality in the rat model of weightlessness by regulating various amino acids and energy metabolism-related pathways.

BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

During long-duration manned space missions, the complex and extreme space environment exerts significant impacts on astronauts' physiological, psychological, and cognitive functions, thereby posing direct risks to mission safety and operational efficiency. As a key bridge between the brain and external devices, brain-computer interface (BCI) technology enables precise acquisition and interpretation of neural signals, offering a novel paradigm for human-machine collaboration in manned spaceflight. Non-invasive BCI technology shows broad application prospects across astronaut selection, mission training, in-orbit task execution, and post-mission rehabilitation. During mission preparation, multimodal signal assessment and neurofeedback training based on BCI can effectively enhance cognitive performance and psychological resilience. During mission execution, BCI can provide real-time monitoring of physiological and psychological states and enable intention-based device control, thereby improving operational efficiency and safety. In the post-mission rehabilitation phase, non-invasive BCI combined with neuromodulation may improve emotional and cognitive functions, support motor and cognitive recovery, and contribute to long-term health management. However, the application of BCI in space still faces challenges, including insufficient signal robustness, limited system adaptability, and suboptimal data processing efficiency. Looking forward, integrating multimodal physiological sensors with deep learning algorithms to achieve accurate monitoring and individualized intervention, and combining BCI with virtual reality and robotics to develop intelligent human-machine collaboration models, will provide more efficient support for space missions.
Brain-Computer Interfaces ; Humans ; Space Flight ; Astronauts/psychology* ; Neurofeedback ; Cognition ; Electroencephalography ; Man-Machine Systems

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

[This corrects the article DOI: 10.1016/j.apsb.2021.07.024.].

Neuropathic pain is frequently comorbidity with cognitive deficits. Neuralized1 (Neurl1)-mediated ubiquitination of CPEB3 in the hippocampus is critical in learning and memory. However, the role of Neurl1 in the cognitive impairment in neuropathic pain remains elusive. Herein, we found that lumbar 5 spinal nerve ligation (SNL) in male rat-induced neuropathic pain was followed by learning and memory deficits and LTP impairment in the hippocampus. The Neurl1 expression in the hippocampal CA1 was decreased after SNL. And this decrease paralleled the reduction of ubiquitinated-CPEB3 level and reduced production of GluA1 and GluA2. Overexpression of Neurl1 in the CA1 rescued cognitive deficits and LTP impairment, and reversed the reduction of ubiquitinated-CPEB3 level and the decrease of GluA1 and GluA2 production following SNL. Specific knockdown of Neurl1 or CPEB3 in bilateral hippocampal CA1 in naïve rats resulted in cognitive deficits and impairment of synaptic plasticity. The rescued cognitive function and synaptic plasticity by the treatment of overexpression of Neurl1 before SNL were counteracted by the knockdown of CPEB3 in the CA1. Collectively, the above results suggest that the downregulation of Neurl1 through reducing CPEB3 ubiquitination and, in turn, repressing GluA1 and GluA2 production and mediating synaptic plasticity impairment in hippocampal CA1 leads to the genesis of cognitive deficits in neuropathic pain.
Animals ; Male ; Neuralgia/metabolism* ; Rats ; Down-Regulation/physiology* ; Ubiquitination/physiology* ; Neuronal Plasticity/physiology* ; Rats, Sprague-Dawley ; CA1 Region, Hippocampal/metabolism* ; Cognitive Dysfunction/metabolism* ; RNA-Binding Proteins/metabolism* ; Receptors, AMPA/metabolism*